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The Tibetan Plateau (TP) has been the focus of numerous studies examining the energy and water cycle variations, but there is still a lack of long-term, quantitative precise assessments of evapotranspiration. This research first provided two sets of long-term comprehensive observational datasets, and an advanced monitoring technique to measure soil moisture, which can improve the estimation accuracy of evapotranspiration. Subsequently, using microwave data, the Surface Energy Balance System model and Machine Learning methods, it calculated a complete set of long-term evapotranspiration data. At the same time, based on reasonable assumptions, it also estimated the total evaporation from plateau lakes. These findings contribute significantly to the understanding of the relationship between the Asian monsoon, the TP’s physical characteristics, and its atmosphere, thereby improving predictions of water resource variability in the TP. The study’s innovative methodologies and synthesis of diverse data sources provide critical information for informed and sustainable water management strategies in the region. [ABSTRACT FROM AUTHOR]

Microwave remote sensing is the most promising tool for monitoring global-scale near-surface soil moisture distributions. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are made to evaluate their soil moisture products via ground observations, forward microwave transfer simulation, and retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional terrestrial system model TerrSysMP and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify sampling distance required for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling resolution of better than 6 km is required to validate the targeted accuracy of 0.04 cm3/cm3 (70 % confidence level) in SMOS and SMAP over typical midlatitude European regions. The minimum sampling resolution depends on the land-surface inhomogeneity and the meteorological situation, which influence the soil moisture patterns, and ranges from about 7 km to 17 km for a 70 % confidence level for a typical year. At the minimum sampling resolution for a 70 % confidence level also the accuracy of footprint-averaged brightness temperature estimates is equal or better than 15 K/10 K for H/V polarization. Estimates strongly deteriorate with sparser sampling densities, e.g., at 3/9 km with 3/5 sampling sites the confidence level of derived footprint estimates can reach about 0.5–0.6 for soil moisture which is much less than the standard 0.7 requirements for ground measurements. The representativeness of ground-based soil moisture and brightness temperature observations – and thus their required minimum sampling densities – are only weakly correlated in space and time. This study provides a basis for a better understanding of sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements. [ABSTRACT FROM AUTHOR]